{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()\n",
    "\n",
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image\n",
    "\n",
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside `cs231n/classifiers/linear_svm.py`. \n",
    "\n",
    "As you can see, we have prefilled the function `svm_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1**\n",
    "\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vectorized_time_1",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vectorized_time_2"
   },
   "outputs": [],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss. Your code for this part will be written inside `cs231n/classifiers/linear_classifier.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sgd"
   },
   "outputs": [],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "validate"
   },
   "outputs": [],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tuning",
    "tags": [
     "code"
    ]
   },
   "outputs": [],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.39 on the validation set.\n",
    "\n",
    "# Note: you may see runtime/overflow warnings during hyper-parameter search. \n",
    "# This may be caused by extreme values, and is not a bug.\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "\n",
    "# Provided as a reference. You may or may not want to change these hyperparameters\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "import pdb\n",
    "\n",
    "# pdb.set_trace()\n",
    "\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.tight_layout(pad=3)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "test"
   },
   "outputs": [],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline question 2**\n",
    "\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in*  \n"
   ]
  }
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